KAKassius509·Apr 18, 2019
In The Saltiest Users on HNsloria
TextBlob
Python2013–2019
sloria / TextBlob
TextBlob
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
3Distinct people
4Mentions
0Explicit recommenders
9.6KGitHub stars
What to know before exploring
What it is
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year4 dated mentions
Observed mentions: 2013-07-31 02:59:54 UTC to 2019-04-18 18:11:21 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2013 | 2013-01-01 00:00:00 UTC | 2014-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2014 | 2014-01-01 00:00:00 UTC | 2015-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
4 sourcesYou're spot on. This model is based off a dictionary with scored words!
For the curious you can see the dictionary here: https://github.com/sloria/TextBlob/blob/eb08c120d364e9086467...
The package used is a pretty popular one called TextBlob. It is nifty for working with unlabeled data like we have with the HackerNews dataset.
We really focused our definition of saltiness around being a combination of (subjective + negative) comments.
We reduced the impact of (objective + negative) as we feel that criticism, while at times painful, if presented objectively isn't necessarily salty.
We built this model fast (1 week) and have since iterated this week into developing a Fine Tuned BERT model that we are training over a much broader set of toxicity, demographic, and polarity features. The training set is much larger and higher quality so we are
Read the rest
expecting a large jump in precision upon deployment.
I hope the app gave you some good chuckles as you went around though. It's hard to explain how excited I felt when I saw pg_is_a_butt at the top of my pandas data frame the first time I processed the data.
It's doing a little bit right. :)
This is the guy who authored TextBlob!
https://github.com/sloria/TextBlob
In Python Best Practice PatternsTextBlob -- Simplified text processing for Python 2 and 3
4 points · Open discussion